def build_auc_design_matrix(subject,
cluster,
latency,
hemi="Lateralized",
freq_bands=None,
ogl=False):
from glob import glob
from pymeg import aggregate_sr as asr
if not ogl:
filenames = glob(
join(inpath, "S%i_*_%s_agg.hdf" % (subject, "stimulus")))
data = asr.delayed_agg(filenames, hemi=hemi, cluster=cluster)()
else:
filenames = glob(
join(inpath,
"ogl/slimdown/ogl_S%i_*_%s_agg.hdf" % (subject, "stimulus")))
data = asr.delayed_agg(filenames,
hemi="Pair",
cluster=["%s_LH" % cluster,
"%s_RH" % cluster])().copy()
data = data.groupby(["hemi", "trial", "freq"]).mean()
data.head()
hemi = np.asarray(data.index.get_level_values("hemi")).astype("str")
trial = np.asarray(data.index.get_level_values("trial")).astype(int)
freq = np.asarray(data.index.get_level_values("freq")).astype(int)
# cl = hemi.copy()
cl = [cluster] * len(hemi)
index = pd.MultiIndex.from_arrays(
[hemi, cl, trial, freq],
names=["hemi", "cluster", "trial", "freq"])
data.index = index
data.head()
data = pd.pivot_table(data, values=latency, index='trial', columns='freq')
if freq_bands is not None:
data = data.loc[:, freq_bands[0]:freq_bands[1]].mean(1)
trial_index = data.index.get_level_values("trial").unique()
meta = preprocessing.get_meta_for_subject(subject, "stimulus")
meta.set_index("hash", inplace=True)
meta = meta.loc[trial_index]
return data, meta
def apply_across_areas(
decoder,
meta,
filenames,
clusters,
decoder,
hemis=["Pair", "Lateralized", "Averaged"],
ntasks=-1,
):
"""
Run a decoder across a set of clusters, time points and hemis.
Args:
meta: pd.DataFrame
Contains the target colum, indexed by trial.
filenames: list
Filenames containing source reconstructed aggregates.
decoder: function
Function that takes meta data, aggregate data, areas
and latency as input and returns decoding scores
as data frame.
latencies: None or list
List of latencies to decode across. If None
all time points in agg will be used.
"""
set_n_threads(1)
from multiprocessing import Pool
from pymeg import aggregate_sr as asr
areas = clusters.keys()
args = []
for area, hemi in product(areas, hemis):
if hemi == "Pair":
area = [area + "_RH", area + "_LH"]
a = (meta, asr.delayed_agg(filenames, hemi=hemi,
cluster=area), decoder, hemi)
args.append(a)
print("Processing %i tasks" % (len(args)))
from contextlib import closing
scores = []
with closing(Pool(ntasks, maxtasksperchild=1)) as p:
sc = p.starmap(_par_apply_decode, args)
sc = [s for s in sc if s is not None]
scores.extend(sc)
scores = pd.concat(scores)
p.terminate()
return scores
def get_ortho_midc(subject):
"""
Compute orthogonalized decoding values for IPS/PostCeS.
"""
from pymeg import aggregate_sr as asr
from glob import glob
inpath = "/home/nwilming/conf_meg/sr_labeled/aggs"
cluster = "JWG_M1"
# First load low level averaged stimulus data
filenames = glob(join(inpath, "S%i_*_%s_agg.hdf" % (subject, "stimulus")))
data = asr.delayed_agg(filenames,
hemi="Lateralized",
cluster=[cluster] + ["JWG_IPS_PCeS"])()
meta = augment_meta(preprocessing.get_meta_for_subject(
subject, "stimulus"))
scores = ortho_midc_decoder(meta,
data,
"JWG_IPS_PCeS",
"JWG_M1",
latency=1.1)
scores.loc[:, "subject"] = subject
return scores